




"""simple docstring"""





from __future__ import annotations

import time

import numpy as np

A				=							[8, 5, 9, 7]
A				=							[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A				=							[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class   a__					:


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     list[int]   ,	UpperCamelCase_   :     list[list[int]]   ,	UpperCamelCase_   :     list[list[int]]   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       claim_vector
						__UpperCAmelCase						:			Optional[Any]												=       allocated_resources_table
						__UpperCAmelCase						:			Optional[int]												=       maximum_claim_table


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return [
						    sum(p_item[i] for p_item in self.__allocated_resources_table)
						    for i in range(len(self.__allocated_resources_table[0]))
						]


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						return np.array(self.__claim_vector) - np.array(
						    self.__processes_resource_summation())


			def    a_  (   self   :     int):


						"""simple docstring"""




						return [
						    list(np.array(self.__maximum_claim_table[i]) - np.array(UpperCamelCase_))
						    for i, allocated_resource in enumerate(self.__allocated_resources_table)
						]


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						return {self.__need().index(UpperCamelCase_): i for i in self.__need()}


			def    a_  (   self   :     List[Any]   ,	**UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.__need()
						__UpperCAmelCase						:			Tuple												=       self.__allocated_resources_table
						__UpperCAmelCase						:			str												=       self.__available_resources()
						__UpperCAmelCase						:			Any												=       self.__need_index_manager()
						for kw, val in kwargs.items():
									if kw and val is True:
												self.__pretty_data()
						print("_" * 50 + "\n")
						while need_list:
									__UpperCAmelCase						:			List[str]												=       False
									for each_need in need_list:
												__UpperCAmelCase						:			Tuple												=       True
												for index, need in enumerate(UpperCamelCase_):
															if need > available_resources[index]:
																		__UpperCAmelCase						:			Optional[Any]												=       False
																		break
												if execution:
															__UpperCAmelCase						:			Union[str, Any]												=       True
															# get the original index of the process from ind_ctrl db
															for original_need_index, need_clone in need_index_manager.items():
																		if each_need == need_clone:
																					__UpperCAmelCase						:			Optional[Any]												=       original_need_index
															print(F"Process {process_number + 1} is executing.")
															# remove the process run from stack
															need_list.remove(UpperCamelCase_)
															# update available/freed resources stack
															__UpperCAmelCase						:			Tuple												=       np.array(UpperCamelCase_) + np.array(
															    alloc_resources_table[process_number])
															print(
															    "Updated available resource stack for processes: "
															    + " ".join([str(UpperCamelCase_) for x in available_resources]))
															break
									if safe:
												print("The process is in a safe state.\n")
									else:
												print("System in unsafe state. Aborting...\n")
												break


			def    a_  (   self   :     List[str]):


						"""simple docstring"""




						print(" " * 9 + "Allocated Resource Table")
						for item in self.__allocated_resources_table:
									print(
									    F"P{self.__allocated_resources_table.index(UpperCamelCase_) + 1}"
									    + " ".join(F"{it:>8}" for it in item)
									    + "\n")
						print(" " * 9 + "System Resource Table")
						for item in self.__maximum_claim_table:
									print(
									    F"P{self.__maximum_claim_table.index(UpperCamelCase_) + 1}"
									    + " ".join(F"{it:>8}" for it in item)
									    + "\n")
						print(
						    "Current Usage by Active Processes: "
						    + " ".join(str(UpperCamelCase_) for x in self.__claim_vector))
						print(
						    "Initial Available Resources:       "
						    + " ".join(str(UpperCamelCase_) for x in self.__available_resources()))
						time.sleep(1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






"""simple docstring"""



from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class    __a						( __snake_case					,			__snake_case					,			__snake_case							):
				lowerCamelCase     :       Optional[int]			       =[R'h\.\d+\.attn\.bias', R'h\.\d+\.attn\.masked_bias']







				@register_to_config
				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = 5_0257      ,	UpperCAmelCase = 1024      ,	UpperCAmelCase = 768      ,	UpperCAmelCase = 12      ,	UpperCAmelCase = 12      ,	UpperCAmelCase = None      ,	UpperCAmelCase = "gelu_new"      ,	UpperCAmelCase = 0.1      ,	UpperCAmelCase = 0.1      ,	UpperCAmelCase = 0.1      ,	UpperCAmelCase = 1E-5      ,	UpperCAmelCase = 0.0_2      ,	UpperCAmelCase = True      ,	UpperCAmelCase = True      ,	UpperCAmelCase = False      ,	UpperCAmelCase = False      ,	):

					'''simple docstring'''






					super().__init__()

					lowerCAmelCase_      							=			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
						raise ValueError(
						    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
						    F""" `n_embd`: {n_embd} are not equal."""   )

					lowerCAmelCase_      							=			prefix_inner_dim
					lowerCAmelCase_      							=			prefix_hidden_dim

					lowerCAmelCase_      							=			(
					    nn.Linear(self.prefix_inner_dim      ,	self.prefix_hidden_dim   )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					lowerCAmelCase_      							=			(
					    nn.Linear(self.prefix_hidden_dim      ,	UpperCAmelCase   ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					lowerCAmelCase_      							=			GPTaConfig(
					    vocab_size=UpperCAmelCase      ,	n_positions=UpperCAmelCase      ,	n_embd=UpperCAmelCase      ,	n_layer=UpperCAmelCase      ,	n_head=UpperCAmelCase      ,	n_inner=UpperCAmelCase      ,	activation_function=UpperCAmelCase      ,	resid_pdrop=UpperCAmelCase      ,	embd_pdrop=UpperCAmelCase      ,	attn_pdrop=UpperCAmelCase      ,	layer_norm_epsilon=UpperCAmelCase      ,	initializer_range=UpperCAmelCase      ,	scale_attn_weights=UpperCAmelCase      ,	use_cache=UpperCAmelCase      ,	scale_attn_by_inverse_layer_idx=UpperCAmelCase      ,	reorder_and_upcast_attn=UpperCAmelCase      ,	)
					lowerCAmelCase_      							=			GPTaLMHeadModel(UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase = None      ,	UpperCAmelCase = None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			self.transformer.transformer.wte(UpperCAmelCase   )
					lowerCAmelCase_      							=			self.encode_prefix(UpperCAmelCase   )
					lowerCAmelCase_      							=			self.decode_prefix(UpperCAmelCase   )
					lowerCAmelCase_      							=			torch.cat((prefix_embeds, embedding_text)      ,	dim=1   )

					if labels is not None:
						lowerCAmelCase_      							=			self.get_dummy_token(input_ids.shape[0]      ,	input_ids.device   )
						lowerCAmelCase_      							=			torch.cat((dummy_token, input_ids)      ,	dim=1   )
					lowerCAmelCase_      							=			self.transformer(inputs_embeds=UpperCAmelCase      ,	labels=UpperCAmelCase      ,	attention_mask=UpperCAmelCase   )
					if self.prefix_hidden_dim is not None:
						return out, hidden
					else:
						return out







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					return torch.zeros(UpperCAmelCase      ,	self.prefix_length      ,	dtype=torch.intaa      ,	device=UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase   ):

					'''simple docstring'''






					return self.encode_prefix(UpperCAmelCase   )







				@torch.no_grad()
				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			torch.split(UpperCAmelCase      ,	1      ,	dim=0   )
					lowerCAmelCase_      							=			[]
					lowerCAmelCase_      							=			[]
					for feature in features:
						lowerCAmelCase_      							=			self.decode_prefix(feature.to(UpperCAmelCase   )   )  # back to the clip feature
						# Only support beam search for now
						lowerCAmelCase_							,	lowerCAmelCase_      							=			self.generate_beam(
						    input_embeds=UpperCAmelCase      ,	device=UpperCAmelCase      ,	eos_token_id=UpperCAmelCase   )
						generated_tokens.append(output_tokens[0]   )
						generated_seq_lengths.append(seq_lengths[0]   )
					lowerCAmelCase_      							=			torch.stack(UpperCAmelCase   )
					lowerCAmelCase_      							=			torch.stack(UpperCAmelCase   )
					return generated_tokens, generated_seq_lengths







				@torch.no_grad()
				def      lowerCamelCase_     ( self      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None      ,	UpperCAmelCase = 5      ,	UpperCAmelCase = 67      ,	UpperCAmelCase = 1.0      ,	UpperCAmelCase = None      ,	):

					'''simple docstring'''






					lowerCAmelCase_      							=			eos_token_id
					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			None
					lowerCAmelCase_      							=			torch.ones(UpperCAmelCase      ,	device=UpperCAmelCase      ,	dtype=torch.int   )
					lowerCAmelCase_      							=			torch.zeros(UpperCAmelCase      ,	device=UpperCAmelCase      ,	dtype=torch.bool   )

					if input_embeds is not None:
						lowerCAmelCase_      							=			input_embeds
					else:
						lowerCAmelCase_      							=			self.transformer.transformer.wte(UpperCAmelCase   )

					for i in range(UpperCAmelCase   ):
						lowerCAmelCase_      							=			self.transformer(inputs_embeds=UpperCAmelCase   )
						lowerCAmelCase_      							=			outputs.logits
						lowerCAmelCase_      							=			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
						lowerCAmelCase_      							=			logits.softmax(-1   ).log()

						if scores is None:
							lowerCAmelCase_							,	lowerCAmelCase_      							=			logits.topk(UpperCAmelCase      ,	-1   )
							lowerCAmelCase_      							=			generated.expand(UpperCAmelCase      ,	*generated.shape[1:]   )
							lowerCAmelCase_							,	lowerCAmelCase_      							=			next_tokens.permute(1      ,	0   ), scores.squeeze(0   )
							if tokens is None:
								lowerCAmelCase_      							=			next_tokens
							else:
								lowerCAmelCase_      							=			tokens.expand(UpperCAmelCase      ,	*tokens.shape[1:]   )
								lowerCAmelCase_      							=			torch.cat((tokens, next_tokens)      ,	dim=1   )
						else:
							lowerCAmelCase_      							=			-float(np.inf   )
							lowerCAmelCase_      							=			0
							lowerCAmelCase_      							=			scores[:, None] + logits
							seq_lengths[~is_stopped] += 1
							lowerCAmelCase_      							=			scores_sum / seq_lengths[:, None]
							lowerCAmelCase_							,	lowerCAmelCase_      							=			scores_sum_average.view(-1   ).topk(UpperCAmelCase      ,	-1   )
							lowerCAmelCase_      							=			next_tokens // scores_sum.shape[1]
							lowerCAmelCase_      							=			seq_lengths[next_tokens_source]
							lowerCAmelCase_      							=			next_tokens % scores_sum.shape[1]
							lowerCAmelCase_      							=			next_tokens.unsqueeze(1   )
							lowerCAmelCase_      							=			tokens[next_tokens_source]
							lowerCAmelCase_      							=			torch.cat((tokens, next_tokens)      ,	dim=1   )
							lowerCAmelCase_      							=			generated[next_tokens_source]
							lowerCAmelCase_      							=			scores_sum_average * seq_lengths
							lowerCAmelCase_      							=			is_stopped[next_tokens_source]

						lowerCAmelCase_      							=			self.transformer.transformer.wte(next_tokens.squeeze()   ).view(generated.shape[0]      ,	1      ,	-1   )
						lowerCAmelCase_      							=			torch.cat((generated, next_token_embed)      ,	dim=1   )
						lowerCAmelCase_      							=			is_stopped + next_tokens.eq(UpperCAmelCase   ).squeeze()
						if is_stopped.all():
							break

					lowerCAmelCase_      							=			scores / seq_lengths
					lowerCAmelCase_      							=			scores.argsort(descending=UpperCAmelCase   )
					# tokens tensors are already padded to max_seq_length
					lowerCAmelCase_      							=			[tokens[i] for i in order]
					lowerCAmelCase_      							=			torch.stack(UpperCAmelCase      ,	dim=0   )
					lowerCAmelCase_      							=			torch.tensor([seq_lengths[i] for i in order]      ,	dtype=seq_lengths.dtype   )
					return output_texts, seq_lengths